List of usage examples for org.deeplearning4j.earlystopping.saver InMemoryModelSaver InMemoryModelSaver
InMemoryModelSaver
From source file:com.heatonresearch.aifh.examples.ann.LearnAutoMPGBackprop.java
License:Apache License
/** * The main method./*from w w w.jav a 2s . c om*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 0.01; // Setup training data. final InputStream istream = LearnAutoMPGBackprop.class.getResourceAsStream("/auto-mpg.data.csv"); if (istream == null) { System.out.println("Cannot access data set, make sure the resources are available."); System.exit(1); } final NormalizeDataSet ds = NormalizeDataSet.load(istream); istream.close(); // The following ranges are setup for the Auto MPG data set. If you wish to normalize other files you will // need to modify the below function calls other files. // First remove some columns that we will not use: ds.deleteColumn(8); // Car name ds.deleteColumn(7); // Car origin ds.deleteColumn(6); // Year ds.deleteUnknowns(); ds.normalizeZScore(1); ds.normalizeZScore(2); ds.normalizeZScore(3); ds.normalizeZScore(4); ds.normalizeZScore(5); DataSet next = ds.extractSupervised(1, 4, 0, 1); next.shuffle(); // Training and validation data split int splitTrainNum = (int) (next.numExamples() * .75); SplitTestAndTrain testAndTrain = next.splitTestAndTrain(splitTrainNum, new Random(seed)); DataSet trainSet = testAndTrain.getTrain(); DataSet validationSet = testAndTrain.getTest(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainSet.asList(), trainSet.numExamples()); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationSet.numExamples()); // Create neural network. int numInputs = next.numInputs(); int numOutputs = next.numOutcomes(); int numHiddenNodes = 50; MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.MSE).weightInit(WeightInit.XAVIER) .activation("identity").nIn(numHiddenNodes).nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() .epochTerminationConditions(new MaxEpochsTerminationCondition(500)) //Max of 50 epochs .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(25)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); System.out.println(features + ":Prediction(" + predicted + "):Actual(" + labels + ")"); } } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsBackprop.java
License:Apache License
/** * The main method./*ww w .j a v a2s .co m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); System.out.println(trainingSet.get(0).getFeatures().size(1)); System.out.println(validationSet.get(0).getFeatures().size(1)); int numInputs = trainingReader.getNumCols() * trainingReader.getNumRows(); int numOutputs = 10; int numHiddenNodes = 200; // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).regularization(true).dropOut(0.50).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsConv.java
License:Apache License
/** * The main method./*from ww w . j a va 2 s .co m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; int channels = 1; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); int numOutputs = 10; // Create neural network. MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .regularization(true).l2(0.0005).learningRate(0.01).weightInit(WeightInit.XAVIER) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NESTEROVS) .momentum(0.9).list(4) .layer(0, new ConvolutionLayer.Builder(5, 5).nIn(channels).stride(1, 1).nOut(20).dropOut(0.5) .activation("relu").build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2) .stride(2, 2).build()) .layer(2, new DenseLayer.Builder().activation("relu").nOut(500).build()) .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10) .activation("softmax").build()) .backprop(true).pretrain(false); new ConvolutionLayerSetup(builder, 28, 28, 1); MultiLayerConfiguration conf = builder.build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsDropout.java
License:Apache License
/** * The main method./*from ww w. ja va 2 s . c o m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); System.out.println(trainingSet.get(0).getFeatures().size(1)); System.out.println(validationSet.get(0).getFeatures().size(1)); int numInputs = trainingReader.getNumCols() * trainingReader.getNumRows(); int numOutputs = 10; int numHiddenNodes = 100; // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnIrisBackprop.java
License:Apache License
/** * The main method./*from w w w . ja v a 2s .c o m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 0.1; int splitTrainNum = (int) (150 * .75); int numInputs = 4; int numOutputs = 3; int numHiddenNodes = 50; // Setup training data. final InputStream istream = LearnIrisBackprop.class.getResourceAsStream("/iris.csv"); if (istream == null) { System.out.println("Cannot access data set, make sure the resources are available."); System.exit(1); } final NormalizeDataSet ds = NormalizeDataSet.load(istream); final CategoryMap species = ds.encodeOneOfN(4); // species is column 4 istream.close(); DataSet next = ds.extractSupervised(0, 4, 4, 3); next.shuffle(); // Training and validation data split SplitTestAndTrain testAndTrain = next.splitTestAndTrain(splitTrainNum, new Random(seed)); DataSet trainSet = testAndTrain.getTrain(); DataSet validationSet = testAndTrain.getTest(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainSet.asList(), trainSet.numExamples()); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationSet.numExamples()); // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() .epochTerminationConditions(new MaxEpochsTerminationCondition(500)) //Max of 50 epochs .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(25)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); System.out.println(features + ":Prediction(" + findSpecies(labels, species) + "):Actual(" + findSpecies(predicted, species) + ")" + predicted); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }